Bibliographic record
Abstract
Proof load tests have the potential to confirm the structural safety of a component suspected of being substandard. Methodologies are available to revise the reliability index of the suspect component, after it passes a proof load test, that essentially assume that the probability that the actual resistance is less than the proof load is zero. There is some sense among practitioners, however, that “you can always pass a proof load test” and so the current methodologies for updating the reliability index may be unconservative. This paper presents the development of rational criteria for including proof load testing into the safety assessment that account for imperfect repeatability of the test result. The necessary mathematical formulation requires the following steps: Define the likelihood that a particular proof load test can be successfully repeated, i.e., (100-α)%; Partially truncate the lower tail of the resistance distribution such that the cumulative probability corresponding to the load test magnitude equals the probability that the load test will not be successfully repeated, i.e., α%; and, Carry out reliability analyses using the partially truncated resistance distribution. Preliminary findings are presented assuming the load and original resistance distributions are normal. Two example calculations demonstrate the applicability of the method, and indicate ist potential value in determining the necessary test load magnitude to achieve a desired reliability index.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".